Data Privacy

Anonymization is the process of removing personal identifiers from data to protect individual privacy.

A Client Privacy Budget is a framework for managing user data privacy during AI training and deployment.

Client-Side Learning involves processing and learning from data directly on a user's device.

Data anonymization is the process of removing or altering personal information to protect privacy while maintaining data utility.

Data brokers collect, analyze, and sell personal data from various sources.

Data Minimalism is the practice of collecting and using only essential data for decision-making and analysis.

Data obfuscation is a technique used to protect sensitive information by making it unintelligible or difficult to interpret.

Data Privacy refers to the management and protection of personal information from unauthorized access and misuse.

Data retention refers to the policies and practices surrounding the storage and management of data over time.

De-identification is the process of removing or obscuring personal information from data sets.

Differential Privacy is a mathematical framework that ensures individual data privacy while allowing data analysis.

Digital Fingerprinting is a technique used to identify and track devices based on unique device characteristics.

An exposure metric quantifies the risk or potential impact of AI models on sensitive data and user privacy.

Federated Averaging is a decentralized machine learning technique that aggregates model updates from various devices without sharing data.

Federated Averaging Algorithm is a method for training machine learning models across decentralized devices without sharing raw data.

Federated Healthcare AI enables collaborative machine learning across multiple healthcare institutions without sharing sensitive data.

Federated Learning is a machine learning approach that trains algorithms across decentralized devices without sharing raw data.

K-Anonymity is a privacy protection technique that ensures individuals cannot be re-identified in datasets.

L-Diversity is a data privacy technique that protects sensitive information by ensuring diverse sensitive attributes in data sets.

Local sensitivity measures how a small change in input affects the output of a function, often used in data privacy.

Model inversion is a technique used to extract sensitive data from machine learning models.

Online tracking refers to the collection and analysis of user data while they browse the internet.

PII Detection identifies and protects personally identifiable information in data.

AI systems designed to protect user data and maintain confidentiality during processing and analysis.

Redaction is the process of editing text to remove sensitive information before publication.

A method enabling multiple parties to compute aggregated data without revealing individual contributions.

Secure Multi-Party Computation allows parties to jointly compute data while keeping their inputs private.

Split Learning is a collaborative machine learning approach that divides the training process between multiple parties.